Consumers often prefer to know whether and when business establishments are open for business, to allow them to organize and plan their own schedules. In addition, consumers prefer to know how busy a merchant is at a given time, to avoid lines and more efficiently access services. There is no central repository, however, as to when all businesses are open or when the business is busy.
When a credit card is used to pay for a product, the merchant submits a request to an acquirer bank. The acquirer bank then sends a request to an issuer bank that authorizes or declines the transaction. If the transaction is approved, the issuer bank provides an authorization code to the acquirer bank, which notifies the merchant to complete the transaction. Each request and authorization involved in this process includes data about the merchant, the consumer, and the transaction. For example, credit card authorizations may have time stamps, a merchant identifier, a transaction amount, and an account number, among other things. Therefore, credit card authorizations may be used to make inferences about the merchant, the consumer, or the transaction. Particularly nowadays, when millions of credit card transactions are recorded every day, statistical methods can be used to analyze credit card authorization data, make accurate inferences, and observe trends.
Credit card authorizations are normally generated when a merchant is open for operation and serving customers. Typically, merchants submit the credit card authorization requests to the acquirer bank concurrently with a consumer making a purchase. Therefore, credit card authorizations can be used to infer whether the merchant is serving customers and is thus “open.” For example, a restaurant may normally issue multiple credit card authorization requests between 11 am and 1 pm when it serves customers during lunch. Thus, it is possible to infer that the restaurant is open between 11 am and 1 pm, based on credit card authorization requests. On the other hand, the same restaurant may not issue any credit card authorization requests between 1 am and 2 am. Thus, it is possible to infer that the restaurant is “closed” between 1 am and 2 am. Therefore, analysis of credit card authorization data can be used to predict a merchant's hours of operation.
Moreover, the credit card authorization data can also be used to make prediction on how busy a merchant is. For example, if a merchant issues multiple credit card authorizations within an hour, it is possible to infer that the merchant is busy during that hour as many customers are paying services or goods. Alternatively, if a merchant is only issuing a few credit card authorizations, is possible to infer that the merchant is not busy because there is likely not many costumers at that time.
However, making accurate predictions of merchant's hours of operation and merchant's popular hours based on credit card authorization request data alone may be challenging for several reasons. First, there may be an imperfect correlation between credit card authorization data and hours of operation. For example, a restaurant may open at 10 am but only start issuing credit card authorizations at 10:30 am, when the first customer finishes his or her meal and pays. Similarly, customers may pay for their meals in a restaurant as they leave, so the merchant may be busy before the credit card authorization is issued. In these examples, the correlations between hours of operation and authorization requests and between merchant's popular hours and authorization requests are offset and may lead to prediction errors. Second, data repositories of credit card authorizations may store millions of authorizations per day. The large quantity and variety of authorization formats and merchant practices may make it difficult to effectively process the authorization data. Third, the correlations between credit card authorizations and hours of operation or popular hours can be dynamic and may be influenced by externalities. For example, the correlation between authorizations and hours of operation or popular hours may be influenced by merchant location, season, and/or business type. For instance, a merchant may have summer hours of operation that are different to the winter hours of operation. Similarly, a merchant's popular hours may peak during the end of the year holidays. These are some of the difficulties that make prediction of hours of operation challenging, but other variables also affect correlations and predictions.
Moreover, predicting popular or busy hours present additional challenges. For example, different merchants types may have different average service times. For example, fast food restaurants serve clients faster than sit down restaurants. Thus, the same pattern of credit card authorizations does not accurately represent the popular or busy hours for different merchants. Also, because a single customer may pay for many customers, the number of credit card authorizations may not represent how busy is the merchant. Also, some credit card authorizations may be generated without a user being in a merchant. For instance, credit card authorizations of online purchases for take home food in restaurants affect the correlation between credit card authorization use and busy hours in a merchant. These are additional difficulties that make prediction of a merchant's popular hours challenging.
The disclosed machine learning artificial intelligence system and modeling methods address one or more of the problems set forth above and/or other problems in the prior art.